AWS Batch introduces quota management for SageMaker Training jobs to enable cross-team resource sharing
AWS Batch now supports quota management for SageMaker Training jobs, allowing administrators to divide GPU capacity into quota shares organized by team or project. Each quota share can be configured with resource-sharing strategies such as LEND_AND_BORROW, which automatically lends idle capacity to other shares and borrows from them when work is available. The system also supports in-share preemption, enabling high-priority workloads to preempt lower-priority jobs within the same share. This eliminates the need for manual queue management and helps improve GPU utilization across multi-team environments.
This feature directly addresses the growing need for efficient multi-team GPU resource management in AI/ML operations, offering a built-in solution for dynamic capacity sharing and priority scheduling without custom infrastructure.
Source: AWS Machine Learning Blog · aws.amazon.com